Automotive diagnostics allow the owner/driver of a vehicle to identify defect or degraded performance of a vehicular component if the vehicle is not able to maximize its performance efficiently. Majority of the automotive problems are normally identified by trained-automotive technicians as they perform a pass/fail test automotive diagnostics test. Only a handful of automotive problems can be identified by the owner/driver who is not a trained-automotive technician. For example, if the vehicular user interface specifically indicates the automotive problem, the problem can be easily identified without having to perform further testing. However, if the vehicular user interface does not indicate any automotive problem or indicates a general warning, further testing has to be performed by the trained-automotive technicians detect the exact problem. Since many of the automotive problems are not immediately identified or detected by the owner/driver, the current vehicular diagnostic system does not provide the most efficient process. Additionally, the owner/driver or trained-automotive technicians are not able to statistically forecast vehicular component failure in advance. As a result, many owners/drivers face unexpected vehicular breakdown that creates unproductive and unsafe circumstances.
It is an object of the present invention to introduce an automotive predictive failure and alerting system for vehicular parts so that the present invention is able to addresses the shortcomings of the prior problems. More specifically, the vehicular sensors continuously report performance values to the engine control unit (ECU) as the ECU continuously transmits these performance values to a remote server. Then the remote server is able to perform real-time calculations to detect any automotive performance variations and also to calculate a part-performance efficiency for each of vehicular components that is communicably coupled with one of vehicular sensors. The performance variations have the ability to detect small deviations from normal part performance, and check other sensors and correlate trip data to create part and vehicle profile patterns distinguishing between towing, racing, and driving uphill, etc. The remote server then utilizes the part-performance efficiency to determine predictive failure for the respective vehicular part so that the owner/driver can be notified.